How AI Learns to Fix IT Problems by Asking for Feedback
This patent describes an AI system that continuously learns to identify and prevent IT issues by building a map of cause-and-effect relationships, getting human feedback, and automatically updating its understanding.
Original patent title: “Continuous knowledge graph generation using causal event graph feedback”
This patent describes an AI system that continuously learns to identify and prevent IT issues by building a map of cause-and-effect relationships, getting human feedback, and automatically updating its understanding. Granted to Bmc Helix in 2025 with 18 claims, and it is expected to expire in 2042.
Coverage
What does this patent actually cover?
The system creates a "causal graph," which is like a map showing how different events in an IT system are connected. It then asks for feedback on this map, for example, by displaying the causal graph and asking for a simple "yes" or "no" (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 5) if the connections are correct. This feedback, along with information about when and where events happened ("spatiotemporal context"), is fed into a machine learning model (Claim 1). The model uses this to build a "knowledge graph," a deeper understanding of the IT system. The process repeats: the system generates a new causal graph, gets more feedback, and updates the knowledge graph, even at different levels of detail (Claim 1), ultimately allowing an "Information Technology (IT) landscape manager" to find the root cause of problems and predict future issues to stop them before they happen, all without needing a person to step in (Claim 1).
The gap
What does this patent NOT cover?
- Does not cover systems that require manual tuning to determine event cluster boundaries, as the abstractabstractA short summary at the front of the patent describing the invention. Not legally binding.Read more → states it does this "without requiring manual tuning."
- Does not cover systems that only generate a knowledge graph once without a continuous feedback loop and update mechanism.
- Does not cover systems that determine root causes or predict events without using a machine learning model to process feedback and spatiotemporal context.
- Does not cover systems where the IT landscape manager requires human intervention to determine root causes or predict future events.
- Does not cover systems that only use one level of detail for updating the knowledge graph, as it specifies updating at a "second level" different from the "first level" of feedback.
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
Key facts
What made this novel
The noveltynoveltyThe requirement that an invention be different from anything publicly known before its priority date.Read more → lies in the continuous, self-improving loop where a machine learning model constantly refines its understanding of cause-and-effect in an IT system by requesting and processing human feedback on its causal graphs, then using that to update its knowledge graph and predict future problems automatically.
The Patent Drawing
Schematic visualization of the patent's claim structure. Hand-drawn diagrams in progress for each landmark patent.
Where you've seen this
Real-world examples
Automated IT operations (AIOps) platforms
Network monitoring and diagnostics tools
Cloud infrastructure management systems
Predictive maintenance for software systems
Why it matters
The bigger picture
In complex IT environments, finding the real cause of a problem can be like finding a needle in a haystack. This patent aims to automate that process, making IT systems more reliable and efficient. By predicting and preventing issues, it could significantly reduce downtime and operational costs for businesses relying on large-scale IT infrastructure.
Filed
September 23, 2022
Granted
December 23, 2025
Market context
Who's building on this
Companies in this space
Companies like BMC Helix (the assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →), IBM, Splunk, Dynatrace, and Cisco are active in the AIOps and IT operations management space. They develop solutions that leverage AI and machine learning for root cause analysis and predictive insights in complex IT environments.
Market impact
This type of technology contributes to the growing field of AIOps, aiming to transform how IT operations are managed. It enables a shift from reactive problem-solving to proactive prevention, potentially reducing the need for human IT staff to manually diagnose and fix issues, thereby impacting operational efficiency and reliability across various industries.
Claim 1 — Plain English
What this patent covers
The system creates a "causal graph," which is like a map showing how different events in an IT system are connected. It then asks for feedback on this map, for example, by displaying the causal graph and asking for a simple "yes" or "no" (Claim 5) if the connections are correct. This feedback, along with information about when and where events happened ("spatiotemporal context"), is fed into a machine learning model (Claim 1). The model uses this to build a "knowledge graph," a deeper understanding of the IT system. The process repeats: the system generates a new causal graph, gets more feedback, and updates the knowledge graph, even at different levels of detail (Claim 1), ultimately allowing an "Information Technology (IT) landscape manager" to find the root cause of problems and predict future issues to stop them before they happen, all without needing a person to step in (Claim 1).
The clever bit
The novelty lies in the continuous, self-improving loop where a machine learning model constantly refines its understanding of cause-and-effect in an IT system by requesting and processing human feedback on its causal graphs, then using that to update its knowledge graph and predict future problems automatically.
What it does not cover
- Does not cover systems that require manual tuning to determine event cluster boundaries, as the abstract states it does this "without requiring manual tuning."
- Does not cover systems that only generate a knowledge graph once without a continuous feedback loop and update mechanism.
- Does not cover systems that determine root causes or predict events without using a machine learning model to process feedback and spatiotemporal context.
- Does not cover systems where the IT landscape manager requires human intervention to determine root causes or predict future events.
- Does not cover systems that only use one level of detail for updating the knowledge graph, as it specifies updating at a "second level" different from the "first level" of feedback.
Patent timeline
Application submitted to the patent office
Application published, typically 18 months after filing
Patent officially issued
Patent enters public domain
PatentBrief Score
Impact Score
Early stage
Citation count
0/40
No citations yet
Claim breadth
12/20
Broad claimsclaimsThe numbered statements at the end of a patent that legally define what the inventor owns.Read more →
Recency
20/20
Granted within 5 years
Assignee scale
0/20
Independent or smaller assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →
PatentBrief Impact Score — based on citation count, claim breadth, recency, and assignee scale. Not a legal assessment.
Heuristic Value Estimate
What this patent might be worth
$31K – $100K
Midpoint $62K · 16.2 yr remaining · industry ×1.6
Heuristic only — blends forward/backward citation counts, claim scope, time remaining, litigation history, and CPC-derived industry baseline. Real valuations need a professional appraisal.
The original legal language
Original claims
18 claims as filed with the patent office.
Concepts involved
Citations
Patent lineage
Cite this patent
Garapati, S. E., & Giral, E. (2025). How AI Learns to Fix IT Problems by Asking for Feedback (U.S. Patent No. 12,505,360). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/12505360/continuous-knowledge-graph-generation-using-causal-event-graph-feedback
Auto-generated from the patent record. Double-check author order and the issue date against the official USPTO document before submitting.
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Common Questions
Frequently Asked Questions
What does How AI Learns to Fix IT Problems by Asking for Feedback cover?
This patent describes an AI system that continuously learns to identify and prevent IT issues by building a map of cause-and-effect relationships, getting human feedback, and automatically updating its understanding.
Who owns patent US 12505360?
Bmc Helix owns this patent, granted in 2025.
When does this patent expire?
This patent is expected to expire on September 23, 2042, when the invention enters the public domain.
What problem does this patent solve?
In complex IT environments, finding the real cause of a problem can be like finding a needle in a haystack. This patent aims to automate that process, making IT systems more reliable and efficient. By predicting and preventing issues, it could significantly reduce downtime and operational costs for businesses relying on large-scale IT infrastructure.
What does this patent NOT cover?
Does not cover systems that require manual tuning to determine event cluster boundaries, as the abstract states it does this "without requiring manual tuning."
Patent monitoring